Behavioral modeling of microwave components is of great interest to system designers of amplifiers. There are, however, difficulties in characterizing, describing and simulating the nonlinear behavior of amplifiers and other microwave components that are stimulated by signals that have a high peak-to-average ratio and that are within a power range covering the full operating range.
For example, amplifier behavior may be driven into full saturation, which may be characterized as hard nonlinear. Most of the existing approaches are based on Volterra theory and as such rely on polynomial approximations. Polynomial approximations can not easily handle hard nonlinear behavior like full saturation. Also, amplifier behavior shows memory effects. Such memory effects are caused by dynamically varying operating conditions, such as temperature and bias, for example, among other causes. These changes are induced by the input signal itself and vary at relatively slow rates compared to the frequency of the RF carrier. As a consequence, the instantaneous behavior of the amplifier becomes a function not only of the instantaneous value of the input signal, but also of the past values of the input signal, referred to as “long term memory effect”. Conventional approaches, such as simple compression and AM-to-PM (amplitude modulation-to-phase modulation) characteristic or a more advanced Poly-Harmonic Distortion (PHD) model, that can handle hard nonlinear behavior, have no straightforward technique for dealing with memory effects.
What is needed, therefore, is a method of effecting behavior modeling of nonlinear components that overcomes at least the shortcomings of known methods described above.